CSharp Clustering Algorithm 1.0

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Software information
Platform:
Windows 7/Vista/XP/2000, Unix/Linux
Publisher:
EgyFirst Software
Price:
Freeware
File size:
22.97 Mb
Date added:
November 5, 2014
Screenshot:
Product page:
Description from the Publisher

CSHARP Clustering Algorithm is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can find clusters of varying shapes, sizes and densities; even in the presence of noise and outliers and in high dimensional spaces as well. It is based on the idea of letting each data point vote for its K-nearest neighbors and adopting the points with the highest votes as clusters’ seeds. Two clusters can be merged if their link strength is sufficient. Any data point not belonging to a cluster is considered as noise. The results of our experimental study on several data sets are encouraging. A wide range of possible parameters settings yield satisfactory solutions using the major validation indexes. CSHARP solutions have been found, in general, superior to those obtained by DBScan, K-means and Mitosis and competitive with spectral clustering algorithm.

Clustering algorithms partition data objects into a certain number of clusters, where a cluster is described in terms of internal homogeneity and external separation. A new clustering algorithm CSHARP is presented for the purpose of finding clusters of arbitrary shapes and arbitrary densities in high dimensional feature spaces. It can be considered as a variation of the Shared Nearest Neighbor algorithm (SNN), in which each sample data point votes for the points in its k-nearest neighborhood. Sets of points sharing a common mutual nearest neighbor are considered as dense regions/blocks. These blocks are the seeds from which clusters may grow up. Therefore, CSHARP is not a point-to-point clustering algorithm. Rather, it is a block-to-block clustering technique. Much of its advantages come from these facts: Noise points and outliers correspond to blocks of small sizes, and homogeneous blocks highly overlap. This technique is not prone to merge clusters of different densities or different homogeneity. The algorithm has been applied to a variety of low and high dimensional data sets with superior results over existing techniques such as DBScan, K-means, Chameleon, Mitosis and Spectral Clustering. The quality of its results as well as its time complexity, place it at the front of these techniques.

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